Cardiovascular disease remains the leading cause of death in North America, with more than 1.6 million myocardial infarctions (MIs) occurring annually in the United States alone. Although substantial advancement in the care of patients with ischemic heart disease has recently contributed to a 20-25% improvement in survival rate, primary preventive interventions with a similar impact on survival could, in theory, avert more than five times as many cardiovascular deaths each year. Clinicians providing preventive care for apparently healthy individuals therefore have an important opportunity to reduce the adverse impact of cardiovascular disease in the community to the extent that they can identify patients at high risk for future vascular events.
One key problem, however, is that individuals with coronary artery disease (CAD), acute coronary syndromes (ACS) and/or other cardiac conditions such as cardiomyopathy are often difficult to identify. Moreover, up to one third of life-threatening cardiovascular events occur in the absence of traditional CAD risk factors. These considerations have led researchers to seek out improved noninvasive diagnostic techniques that can better screen for and predict cardiovascular events.
Diagnosis of abnormal cardiac conditions has relied in the past on visible alterations in the P, QRS, and T-waves, i.e. portions of the electrocardiograph periodic signal. The electrocardiograph signal includes a low frequency (LF) portion and an impressed or imbedded high frequency (HF) portion, and it has been found that although the HF portion of the signal is not particularly visible, it contains information that provides greater sensitivity in determining certain abnormalities, notably myocardial ischemia and infarction and cardiomyopathy of any etiology.
The conventional surface electrocardiogram (ECG) has long been used as a diagnostic tool for detecting problems with the heart. A representative limb-lead tracing from a conventional surface ECG in a healthy subject is shown in FIG. 1. Although the conventional ECG is useful, a significant percentage of individuals presenting to a hospital emergency room with an actual heart attack will nonetheless have a normal 12-lead conventional ECG. In addition, the conventional ECG accurately reflects only the predominant LF electrical activity of the heart, as illustrated in FIG. 1. It tells the clinician little or nothing about the less predominant (lower amplitude) HF components of the heart's electrical signal embedded within the various lower-frequency waves of the conventional ECG.
As shown in FIG. 1, the graphic representation of signals that constitute the conventional tracing is limited to visible frequencies of about 100 Hz and lower. On the other hand, higher component frequencies are known to exist within the ECG signal, but are not visible to interpreting clinicians possessing only conventional ECG equipment. To accurately detect the HFs (especially those in the important 150-250 Hz range), digital sampling rates that exceed those of most conventional ECG manufacturers are first required. Traditionally, a clinician looks for changes in the ST segments of conventional LF ECG tracings as a potential evidence for myocardial ischemia. However, during actual ischemia, diminution of the HF components within the QRS complex has been shown to occur well prior to any changes in the conventional ST segments. Thus, the visualization of HF changes within the entire QRS complex represents potentially a much more sensitive way to non-invasively detect myocardial ischemia in contrast to current conventional ECG techniques.
From off-line studies, it is known that a diminution of the amplitude of the HF components within the central portion of the QRS complex of the ECG can be a highly sensitive indicator for the presence of myocardial ischemia, myocardial infarction, or cardiomyopathy, more sensitive, for example, than changes in the ST segment of the conventional ECG. However, until now, there has been no device capable of displaying, in real time, changes in these HF QRS components in the monitored patient. While academic software programs have been designed that analyze the central HF QRS components, all such programs involve laborious off-line calculations and post-processing, and therefore have little if any clinical utility, being strictly research tools.
Thus, there remains a need for a system and method that analyzes HF components over the entire QRS interval in real time for usefulness in the clinical environment. Such a system should perform, in real time, all of the complex digital sampling, averaging, and filtering that is required to generate HF QRS ECG signals. The system should also thereafter update these HF QRS ECG signals, as well as other derived parameters, in real time on a beat-to-beat basis, supplementing the diagnostic information being obtained from the conventional (i.e. low frequency) ECG complexes at the same time.
The HF signals in the central portion of the QRS ECG complex that have generated the most research interest in terms of off-line detection of ischemia and infarction are those signals in the range of 150 to 250 Hz. The raw, analog ECG signal is typically sampled at ≧500 samples per second (to digitize the signal) in order to adequately satisfy the Nyquist rate of sampling at at least twice the highest frequency of interest and in order to retain the information in the signal without loss. In the past, the sampled data have been stored, and then later processed to provide potentially useful information to the researcher.
On the other hand, Simpson, in U.S. Pat. No. 4,422,459, teaches a system which analyzes only the late portion of the QRS interval and early portion of the ST segment, and in an off-line fashion (i.e. from previously stored data) to indicate cardiac abnormalities, in particular the propensity for cardiac arrhythmia. The late portion of a post-MI patient's QRS waveform can contain a broader range (40-250 Hz) HF signal tail potentially indicative of a tendency toward ventricular tachycardia. The system in Simpson digitally processes and filters a patient's QRS signals in a reverse time manner to isolate the HF tail and avoid the filter ringing which would otherwise hide the signal. Thus, in order to do so, Simpson presupposes that the data are stored so that they can be processed in reverse time order.
Albert et al., U.S. Pat. No. 5,117,833, partially focuses on analyzing signals within the mid-portion of the QRS interval for the indication of cardiac abnormality. The system of Albert et al. uses a known technique of building up data points to derive an average of heartbeat characteristics in order to enhance signal to noise ratio. Data are collected and filtered and then stored for subsequent analysis. Thus, the system does not teach a cardiac monitor which provides the data analysis immediately from the data derived from a patient, i.e. in “real-time”.
Albert et al., U.S. Pat. No. 5,046,504, similarly teaches the acquisition of QRS data and subsequent analysis. Routine calculations are performed from the data previously calculated and stored. Further, this system teaches producing a set of digital spectrum values representative of an approximate power density spectrum at each of a large number of generally equally spaced sampling time intervals of the ECG waveform.
Seegobin, in U.S. Pat. Nos. 5,655,540 and 5,954,664, provides a method for identifying CAD. The method relies on a database of various frequency ECG data taken from known healthy and diseased subjects. Comparison of the data has led to a “Score” component, indicating deviation of a patient's data from the norm. This reference is rather calculation intensive, relies strictly on signal amplitudes rather than signal morphologies, and does not suggest monitoring the condition of a patient, but rather is utilized as an off-line diagnostic tool.
Hutson, U.S. Pat. No. 5,348,020, teaches a technique of near real-time analysis and display. The technique includes inputting ECG data from multiple, sequential time intervals and formatting those data into a two-dimensional matrix. The matrix is then decomposed to obtain corresponding singular values and vectors for data compression. The compressed form of the matrix is analyzed and filtered to identify and enhance ECG signal components of interest. As with other systems, this reference focuses on late potentials, a fraction of the QRS interval, as the tool to identify cardiac disease.
Finally, High-Frequency Electrocardiogram Analysis of the Entire QRS in the Diagnosis and Assessment of Coronary Artery Disease by Abboud (Progress in Cardiovascular Diseases, Vol. XXXV, No. 5 (March/April), 1993: pp 311-328) teaches the concept of “reduced amplitude zone” (RAZ) as a diagnostic tool. However, this reference also uses post-processing, and provides no teaching of a real-time analysis system. In the disclosure to follow, the reduced amplitude zone as defined in this work may be referred to as RAZA. Further, Abboud et al. used only three of the standard 12 ECG lead positions (specifically leads V3, V4 and V5) and defined a positive test for CAD as one in which a RAZA was present in at least two of these three selected precordial leads. Using a group of cardiac-catheterized patients who had presented with chest pain but with a normal conventional 12-lead ECG, the sensitivity and specificity of this 3 precordial-lead HF QRS ECG test for identifying significant CAD (i.e., >50% stenosis in a major coronary artery) were 75% and 80%, respectively.
Thus, there remains a need for an electrocardiograph that analyzes, in real time, the HF components of the entire QRS complex in order to provide an effective monitor for patients with possible cardiac abnormalities. Further, there remains a need for an electrocardiograph that receives the analysis of the HF components of the QRS in more than three leads (preferably in 12 or more) and automatically and sensitively provides a clear and substantially unambiguous indication of cardiac abnormalities. The present invention is directed to such an electrocardiograph.